East Boston Demographics
Naomi, Abdul
Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.
library(leaflet)
library(sf)## Linking to GEOS 3.8.1, GDAL 3.2.1, PROJ 7.2.1library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.3 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 2.0.1 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(tidycensus)
library(ggthemes)
library(ggspatial)
library(htmlwidgets)
library(tidytransit)vars2010 <- load_variables(2010, "sf1")boston_latinx <- get_decennial(geography = "tract",
state = "MA", county = "suffolk",
year = 2010,
output = "wide",
variables = c(tot_pop = 'P004001',
la_pop = 'P004003'),
geometry = TRUE)## Getting data from the 2010 decennial Census## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.## Using Census Summary File 1##
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|======================================================================| 100%boston_latinx <- boston_latinx %>%
mutate(pct_latinx = la_pop / tot_pop) %>%
filter(tot_pop > 0)acs_vars <- load_variables(2019, "acs5")v19 <- load_variables(2019, "acs5", cache = TRUE)##Latinx Population in East Boston (Interactive) - Census Blocks
MA_latinx_palette <- colorNumeric(c("lightpink", "yellow"), boston_latinx$pct_latinx)
latinx_map1 <- leaflet(boston_latinx) %>%
addProviderTiles("Stamen.TonerLite") %>%
addPolygons(color = ~MA_latinx_palette(pct_latinx), stroke = FALSE, fillOpacity = 0.7,
highlightOptions = highlightOptions(fillColor = "orange", fillOpacity = 0.9),
label = boston_latinx$NAME,
popup = paste("Total population: ", boston_latinx$tot_pop, "<br/>",
"Latinx population: ", boston_latinx$la_pop, " (",
round(boston_latinx$pct_latinx * 100, 1), "%)", sep = "")) %>%
addLegend("bottomright", pal = MA_latinx_palette, values = ~pct_latinx,
title = "Percent of population<br/>identifying as Hispanic or Latinx",
labFormat = labelFormat(suffix = "%",
transform = function(x) 100 * x),
opacity = 1)## Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
## Need '+proj=longlat +datum=WGS84'latinx_map1##Latinx Population (Static Map)
MA_state_plane <- "+proj=lcc +lat_1=42.68333333333333 +lat_2=41.71666666666667 +lat_0=41 +lon_0=-71.5 +x_0=200000.0001016002 +y_0=750000 +ellps=GRS80 +datum=NAD83 +to_meter=0.3048006096012192 +no_defs"
ggplot(boston_latinx) +
annotation_map_tile(zoomin = 0, progress = "none", type = "stamenbw") +
geom_sf(color = NA, aes(fill = pct_latinx), alpha = 0.7) +
coord_sf(crs = MA_state_plane) +
scale_fill_continuous(low="cornsilk", high="darkgreen",
na.value=NA,
name = "Percent of population\nidentifying as Latinx",
breaks = c(0, 0.2, 0.4, 0.6, 0.8, 1),
labels = c("0", "20%", "40%", "60%", "80%", "100%")) +
theme_void() ## Loading required namespace: raster
saveWidget(latinx_map1, file="latinx_map_TRACT.html")boston_latinx <- get_decennial(geography = "block",
state = "MA", county = "suffolk",
year = 2010,
output = "wide",
variables = c(tot_pop = 'P004001',
la_pop = 'P004003'),
geometry = TRUE)## Getting data from the 2010 decennial Census## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.## Using Census Summary File 1##
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|======================================================================| 100%boston_latinx <- boston_latinx %>%
mutate(pct_latinx = la_pop / tot_pop) %>%
filter(tot_pop > 0)##Latinx Population - Census Blocks
MA_latinx_palette <- colorNumeric(c("lightpink", "yellow"), boston_latinx$pct_latinx)
latinx_map_TRACT <- leaflet(boston_latinx) %>%
addProviderTiles("Stamen.TonerLite") %>%
addPolygons(color = ~MA_latinx_palette(pct_latinx), stroke = FALSE, fillOpacity = 0.7,
highlightOptions = highlightOptions(fillColor = "orange", fillOpacity = 0.9),
label = boston_latinx$NAME,
popup = paste("Total population: ", boston_latinx$tot_pop, "<br/>",
"Latinx population: ", boston_latinx$la_pop, " (",
round(boston_latinx$pct_latinx * 100, 1), "%)", sep = "")) %>%
addLegend("bottomright", pal = MA_latinx_palette, values = ~pct_latinx,
title = "Percent of population<br/>identifying as Hispanic or Latinx",
labFormat = labelFormat(suffix = "%",
transform = function(x) 100 * x),
opacity = 1)## Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
## Need '+proj=longlat +datum=WGS84'latinx_map_TRACTsaveWidget(latinx_map_TRACT, file="latinx_map_BLOCK.html")medianlatinx_Suffolk <- get_acs(geography = "tract", county = "Suffolk", state = "MA",
year = 2019, survey = "acs5",
variables = c(latinx_income = "B19013I_001"),
output = "wide", geometry = TRUE) ## Getting data from the 2015-2019 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.##
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|======================================================================| 100%medianlatinx_Suffolk <- medianlatinx_Suffolk %>%
select(-latinx_incomeM) %>%
filter(latinx_incomeE > 0)income_palette <- colorNumeric(c("purple", "lightblue"),
medianlatinx_Suffolk$latinx_incomeE)
medianincome_map <- leaflet(medianlatinx_Suffolk) %>%
addProviderTiles("Stamen.TonerLite") %>%
addPolygons(fillColor = ~income_palette(latinx_incomeE), weight = 1, color = "gray", fillOpacity = 0.7,
highlightOptions = highlightOptions(fillColor = "yellow", fillOpacity = 0.9),
label = medianlatinx_Suffolk$NAME,
popup = paste("Median Income: ", medianlatinx_Suffolk$latinx_incomeE, "<br/>")) %>%
addLegend("bottomright", pal = income_palette, values = ~latinx_incomeE,
title = "Median Household Income<br/>for Latinx Household",
labFormat = labelFormat(suffix = "$"),
opacity = 1)## Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
## Need '+proj=longlat +datum=WGS84'medianincome_mapsaveWidget(medianincome_map, file="medianincome_map_TRACT.html")latinx_pop_Suffolk <- get_acs(geography = "tract", county = "Suffolk", state = "MA",
year = 2019, survey = "acs5",
variables = c(tot_pop = "B01003_001", latinx_pop = "B03001_001"),
output = "wide", geometry = TRUE) ## Getting data from the 2015-2019 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.latinx_pop_Suffolk <- latinx_pop_Suffolk %>%
filter(tot_popE > 0) %>%
mutate(pct_latinx = round(100 * latinx_popE / tot_popE, 1))latinx_pop_palette <- colorNumeric(c("lightpink", "yellow"),
latinx_pop_Suffolk$pct_latinx)
latinx_pop_map <- leaflet(latinx_pop_Suffolk) %>%
addProviderTiles("Stamen.TonerLite") %>%
addPolygons(fillColor = ~latinx_pop_palette(pct_latinx), weight = 1, color = "gray", fillOpacity = 0.7,
highlightOptions = highlightOptions(fillColor = "yellow", fillOpacity = 0.9),
label = latinx_pop_Suffolk$NAME,
popup = paste("Total workers: ", latinx_pop_Suffolk$tot_popE, "<br/>",
"Transit commuters: ", latinx_pop_Suffolk$latinx_popE, " (",
latinx_pop_Suffolk$pct_latinx, "%)", sep = "")) %>%
addLegend("bottomright", pal = latinx_pop_palette, values = ~pct_latinx,
title = "Percent of population<br/>identifying as Hispanic/Latinx",
labFormat = labelFormat(suffix = "%"),
opacity = 1)## Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
## Need '+proj=longlat +datum=WGS84'latinx_pop_map